Associations between prenatal metal exposure and growth rate in children: Based on Hangzhou Birth Cohort Study.

BACKGROUND: It has been reported that prenatal metal exposure is associated with child anthropometry. However, studies focusing on the growth rate of anthropometry among children have not been conducted. This study aimed to examine associations between the exposure of multiple metals during pregnancy and the growth rate of anthropometry among offspring. METHODS: 743 mother-child pairs from the Hangzhou Birth Cohort Study (HBCS) were included. Levels of eleven metals in mother's blood during pregnancy were measured. Offspring had a mean of 5.7 measurements on anthropometric indicators including weight, length/height, head circumference, and body mass index (BMI) within 1.5 years of birth. Generalized estimating equation (GEE) model was used to investigate the associations between maternal metal exposure and growth rate of anthropometric indicators in children. Stratification analysis by sex was also examined. RESULTS: Levels of selenium (Se, ß = 0.213, 95 % CI = 0.017 to 0.409, P = 0.033) were positively associated with length/height gain per month in children. Levels of chromium (Cr, ß = 0.025, 95 % CI = 0.018 to 0.033, P < 0.001) were positively associated with the rate of weight gain. Levels of manganese (Mn, ß = -0.030, 95 % CI = -0.052 to -0.008, P = 0.009) and cobalt (Co, ß = -0.012, 95 % CI = -0.024 to -0.000, P = 0.044) were inversely associated with growth rate of head circumference. Children with higher maternal Mn levels had a lower BMI change rate. Associations between metals and growth rate were stronger in girls than in boys. Besides, significant associations between metal mixtures and growth rate were found. CONCLUSION: Prenatal exposure to Se, Cr, Mn, and Co was associated with growth rate in children, with sex-specific disparities. Our results suggested important effects of maternal exposure to multiple metals on development in offspring.

The Disease and Gene Annotations (DGA): an annotation resource for human disease.

Disease and Gene Annotations database (DGA, http://dga.nubic.northwestern.edu) is a collaborative effort aiming to provide a comprehensive and integrative annotation of the human genes in disease network context by integrating computable controlled vocabulary of the Disease Ontology (DO version 3 revision 2510, which has 8043 inherited, developmental and acquired human diseases), NCBI Gene Reference Into Function (GeneRIF) and molecular interaction network (MIN). DGA integrates these resources together using semantic mappings to build an integrative set of disease-to-gene and gene-to-gene relationships with excellent coverage based on current knowledge. DGA is kept current by periodically reparsing DO, GeneRIF, and MINs. DGA provides a user-friendly and interactive web interface system enabling users to efficiently query, download and visualize the DO tree structure and annotations as a tree, a network graph or a tabular list. To facilitate integrative analysis, DGA provides a web service Application Programming Interface for integration with external analytic tools.